Bibliographic record
Abstract
This presentation describes a recent study that explored audio-haptic perception in group performance contexts. Vocalizing and listening activities were structured using methods from the Deep Listening practice developed by Pauline Oliveros, with augmentations to the immersive environment made using a multi-channel audio system, in combination with under-floor and body-worn vibrotactile transducers. Bio-signals from participants were captured and qualitative feedback solicited in order to understand the perceptual and physiological effects of introducing signals to these structured performative activities that are dynamically spatialized in audio as well as haptic sense modalities. The study compares the differences in response when these signals are driven by an external source in comparison to being driven by the bio-signals of participants, including measures focused on convergence and divergence of bio-phyiscal activity. The presentation will compare these scenarios and discuss the larger implications for developing immersive interactive environments that contribute to a sense of connection, sociality and play.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".